Abstract
Personalized Recommender System for Arabic News on Twitter
Highlights
The magnitude of data generated on the Internet by different business communities, administrations, industrial sectors, scientific research and general data has increased immeasurably [1]
The text categorization to assign a category to Arabic language content on the Internet and in social media lags behind the actual outcomes, since there are no firm rules for Arabic language understanding by machine learning systems [5]
Our proposed system consists of three parts: collaborative recommender system which is popularity-based, content-based recommender system which is profile-based and hybrid recommender system which is the combination of the two previous systems
Summary
The magnitude of data generated on the Internet by different business communities, administrations, industrial sectors, scientific research and general data has increased immeasurably [1]. This situation is due to the significant number of Arabic languages subcategories with different accents and meanings, which cause the listener to misconception the idea [4]. The text categorization to assign a category to Arabic language content on the Internet and in social media lags behind the actual outcomes, since there are no firm rules for Arabic language understanding by machine learning systems [5]. Part of these massive Arabic data on social media and other platforms is news. Different other platforms are being used in news field, but not all of these platforms are authentic and reliable for the news and data collected
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